Abstract
Urban rail transit (URT) plays a pivotal role in mitigating urban congestion and emissions, positioning it as a sustainable transportation alternative. Nevertheless, URT’s function in transporting substantial numbers of passengers within confined public spaces renders it vulnerable to the proliferation of infectious diseases during public health crises. This study proposes a decision support model that integrates operational control strategies pertaining to passenger flow and train capacity utilization, with an emphasis on energy efficiency within URT networks during such crises. The model anticipates a URT system where passengers adhere to prescribed routes, adhering to enhanced path flow regulations. Simultaneously, train capacity utilization is intentionally limited to support social distancing measures. The model’s efficacy was assessed using data from the COVID-19 outbreak in Xi’an, China, at the end of 2021. Findings indicate that focused management of passenger flows and specific risk areas is superior in promoting energy efficiency and enhancing passenger convenience, compared to broader management approaches.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Canca D, Zarzo A (2017). Design of energy-efficient timetables in two-way railway rapid transit lines. Transportation Research Part B: Methodological, 102: 142–161
de Palma A, Vosough S, Liao F (2022). An overview of effects of COVID-19 on mobility and lifestyle: 18 months since the outbreak. Transportation Research Part A, Policy and Practice, 159: 372–397
Elias W, Zatmeh-Kanj S (2021). Extent to which COVID-19 will affect future use of the train in Israel. Transport Policy, 110: 215–224
Gao Z, Yang L (2019). Energy-saving operation approaches for urban rail transit systems. Frontiers of Engineering Management, 6(2): 139–151
Gkiotsalitis K, Cats O (2022). Optimal frequency setting of metro services in the age of COVID-19 distancing measures. Transportmetrica A: Transport Science, 18(3): 807–827
Guo X, Wang D Z, Wu J, Sun H, Zhou L (2020). Mining commuting behavior of urban rail transit network by using association rules. Physica A, 559: 125094
Harris J E (2020). The subways seeded the massive coronavirus epidemic in New York City. National Bureau of Economic Research
He J, Yan N, Zhang J, Yu Y, Wang T (2022). Battery electric buses charging schedule optimization considering time-of-use electricity price. Journal of Intelligent and Connected Vehicles, 5(2): 138–145
Huang K, Liao F (2023). A novel two-stage approach for energy-efficient timetabling for an urban rail transit network. Transportation Research Part E, Logistics and Transportation Review, 176: 103212
Huang K, Liao F, Gao Z (2021). An integrated model of energy-efficient timetabling of the urban rail transit system with multiple interconnected lines. Transportation Research Part C, Emerging Technologies, 129: 103171
Huang K, Liao F, Lyu H, Gao Z (2023). Assessment of the tradeoff between energy efficiency and transfer opportunities in an urban rail transit network. Sustainable Energy Technologies and Assessments, 58: 103360
Huang Y, Yang L, Tang T, Gao Z, Cao F (2017). Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks., 138: 1124–1147
Ji J, Bie Y, Zeng Z, Wang L (2022). Trip energy consumption estimation for electric buses. Communications in Transportation Research, 2: 100069
Jia J, Chen Y, Wang Y, Li T, Li Y (2021). A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China. Physica A, 565: 125578
Kang L, Sun H, Wu J, Gao Z (2020). Last train station-skipping, transfer-accessible and energy-efficient scheduling in subway networks., 206: 118127
Liao F (2021). Exact space–time prism of an activity program: bidirectional searches in multi-state supernetwork. International Journal of Geographical Information Science, 35(10): 1975–2001
Liao F, Arentze T, Timmermans H (2014). Multi-state supernetworks: recent progress and prospects. Journal of Traffic and Transportation Engineering, 1(1): 13–27
Lu Y, Yang L, Yang K, Gao Z, Zhou H, Meng F, Qi J (2022). A distributionally robust optimization method for passenger flow control strategy and train scheduling on an urban rail transit line. Engineering, 12: 202–220
Luo Q, Forscher T, Shaheen S, Deakin E, Walker J L (2023). Impact of the COVID-19 pandemic and generational heterogeneity on ecommerce shopping styles—A case study of Sacramento, California. Communications in Transportation Research, 3: 100091
Lv H, Zhang Y, Huang K, Yu X, Wu J (2019). An energy-efficient timetable optimization approach in a bi-direction urban rail transit line: A mixed-integer linear programming model. Energies, 12(14): 2686
Mo P, Yang L, D’Ariano A, Yin J, Yao Y, Gao Z (2020). Energy-efficient train scheduling and rolling stock circulation planning in a metro line: A linear programming approach. IEEE Transactions on Intelligent Transportation Systems, 21(9): 3621–3633
Ning J, Zhou Y, Long F, Tao X (2018). A synergistic energy-efficient planning approach for urban rail transit operations. Energy, 151: 854–863
Pan D, Zhao L, Luo Q, Zhang C, Chen Z (2018). Study on the performance improvement of urban rail transit system. Energy, 161: 1154–1171
Qin J, Liao F (2021). Space-time prism in multimodal supernetwork-Part 1: Methodology. Communications in Transportation Research, 1: 100016
Sun X, Wandelt S, Zhang A (2023). Why are COVID-19 travel bubbles a tightrope walk? An investigation based on the Trans-Tasmanian case. Communications in Transportation Research, 3: 100089
Wang L, Yang L, Gao Z, Huang Y (2017). Robust train speed trajectory optimization: A stochastic constrained shortest path approach. Frontiers of Engineering Management, 4(4): 408–417
Wang Q, Guo J, Ge Y, Liang C, Xian K, Diao J, Zhang L, Ma Y (2021). Practice and thoughts on reservation travel in Beijing Metro Stations. Urban Transp. China, 19: 89–94
Xie J, Zhang J, Sun K, Ni S, Chen D (2021). Passenger and energy-saving oriented train timetable and stop plan synchronization optimization model. Transportation Research Part D, Transport and Environment, 98: 102975
Yin J, Cao X J, Huang X (2021). Association between subway and life satisfaction: Evidence from Xi’an, China. Transportation Research Part D, Transport and Environment, 96: 102869
Zeng G, Sun Z, Liu S, Chen X, Li D, Wu J, Gao Z (2021). Percolation-based health management of complex traffic systems. Frontiers of Engineering Management, 8(4): 557–571
Zhang L, He D, He Y, Liu B, Chen Y, Shan S (2022). Real-time energy saving optimization method for urban rail transit train timetable under delay condition. Energy, 258: 124853
Zhang P, Yang X, Wu J, Sun H, Wei Y, Gao Z (2023). Coupling analysis of passenger and train flows for a large-scale urban rail transit system. Frontiers of Engineering Management, 10(2): 250–261
Zhou J, Koutsopoulos H N (2021). Virus transmission risk in urban rail systems: microscopic simulation-based analysis of spatio-temporal characteristics. Transportation Research Record: Journal of the Transportation Research Board, 2675(10): 120–132
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing Interests The authors declare that they have no competing interests.
Additional information
This work is jointly supported by the National Natural Science Foundation of China (Grant Nos. 72288101, 72101018, 72271127, 71801134) and the Dutch Research Council (NWO). The first author is grateful to the financial support by the China Scholarship Council (CSC).
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Huang, K., Liao, F., Rasouli, S. et al. Toward energy-efficient urban rail transit with capacity constraints under a public health emergency. Front. Eng. Manag. (2024). https://doi.org/10.1007/s42524-024-3088-9
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42524-024-3088-9